{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled12.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPhvgPwkNScFX3kd64VHnc4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JacopoMangiavacchi/TestCoreMLTools/blob/master/TF1/TestTensorFlow1x.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx_Zyvu2e3T1",
        "colab_type": "code",
        "outputId": "9cf6dbcb-3318-4529-94e3-6319b86adc18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "!pip install tensorflow==1.12.0\n",
        "!pip install coremltools"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.12.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/cc/ca70b78087015d21c5f3f93694107f34ebccb3be9624385a911d4b52ecef/tensorflow-1.12.0-cp36-cp36m-manylinux1_x86_64.whl (83.1MB)\n",
            "\u001b[K     |████████████████████████████████| 83.1MB 74kB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.0.8)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.2.2)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (3.10.0)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.9.0)\n",
            "Collecting tensorboard<1.13.0,>=1.12.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/07/53/8d32ce9471c18f8d99028b7cef2e5b39ea8765bd7ef250ca05b490880971/tensorboard-1.12.2-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1MB 33.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.34.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.12.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.8.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.17.5)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.27.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.12.0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.12.0) (45.1.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (1.0.0)\n",
            "Installing collected packages: tensorboard, tensorflow\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow 1.15.0\n",
            "    Uninstalling tensorflow-1.15.0:\n",
            "      Successfully uninstalled tensorflow-1.15.0\n",
            "Successfully installed tensorboard-1.12.2 tensorflow-1.12.0\n",
            "Collecting coremltools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/19/611916d1ef326d38857d93af5ba184f6ad7491642e0fa4f9082e7d82f034/coremltools-3.3-cp36-none-manylinux1_x86_64.whl (3.4MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4MB 2.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.17.5)\n",
            "Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (3.10.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.1.0->coremltools) (45.1.0)\n",
            "Installing collected packages: coremltools\n",
            "Successfully installed coremltools-3.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7M-1RR-yP5y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "01c2eef1-180f-4445-a3de-e5b20d5b56dc"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvPyeQUqfWfO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Flatten, Input\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import coremltools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6UZyfTkSWQb",
        "colab_type": "text"
      },
      "source": [
        "# Prepare data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6AOWBlVy-Vi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "108beb58-320d-439a-fb66-5170df3d3adb"
      },
      "source": [
        "dataframe = pd.read_csv(\"./data/housing.csv\", delim_whitespace=True, header=None)\n",
        "dataframe.head(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
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              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        0     1     2   3      4      5   ...  8      9     10      11    12    13\n",
              "0  0.00632  18.0  2.31   0  0.538  6.575  ...   1  296.0  15.3  396.90  4.98  24.0\n",
              "1  0.02731   0.0  7.07   0  0.469  6.421  ...   2  242.0  17.8  396.90  9.14  21.6\n",
              "2  0.02729   0.0  7.07   0  0.469  7.185  ...   2  242.0  17.8  392.83  4.03  34.7\n",
              "3  0.03237   0.0  2.18   0  0.458  6.998  ...   3  222.0  18.7  394.63  2.94  33.4\n",
              "4  0.06905   0.0  2.18   0  0.458  7.147  ...   3  222.0  18.7  396.90  5.33  36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96nOm8h_y_qR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8e6b2ede-989f-44cb-9861-0f10d92ac944"
      },
      "source": [
        "train_dataset = dataframe.sample(frac=0.8,random_state=0)\n",
        "test_dataset = dataframe.drop(train_dataset.index)\n",
        "\n",
        "train_dataset.shape, test_dataset.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((405, 14), (101, 14))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkvCD-ZtzbvP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b1b3ae14-bad2-4976-e666-b735eedd4d8d"
      },
      "source": [
        "train_stats = train_dataset.describe()\n",
        "train_stats.pop(13)\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
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              "      <th>0</th>\n",
              "      <td>405.0</td>\n",
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              "      <td>8.977954</td>\n",
              "      <td>0.00632</td>\n",
              "      <td>0.08265</td>\n",
              "      <td>0.26838</td>\n",
              "      <td>3.47428</td>\n",
              "      <td>88.9762</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>405.0</td>\n",
              "      <td>11.067901</td>\n",
              "      <td>22.688396</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>12.50000</td>\n",
              "      <td>95.0000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>405.0</td>\n",
              "      <td>11.097951</td>\n",
              "      <td>6.699157</td>\n",
              "      <td>0.74000</td>\n",
              "      <td>5.32000</td>\n",
              "      <td>9.69000</td>\n",
              "      <td>18.10000</td>\n",
              "      <td>27.7400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>405.0</td>\n",
              "      <td>0.069136</td>\n",
              "      <td>0.253999</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.0000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>405.0</td>\n",
              "      <td>0.553098</td>\n",
              "      <td>0.115564</td>\n",
              "      <td>0.38500</td>\n",
              "      <td>0.44900</td>\n",
              "      <td>0.53800</td>\n",
              "      <td>0.62400</td>\n",
              "      <td>0.8710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>405.0</td>\n",
              "      <td>6.269768</td>\n",
              "      <td>0.690552</td>\n",
              "      <td>3.56100</td>\n",
              "      <td>5.87800</td>\n",
              "      <td>6.19300</td>\n",
              "      <td>6.61900</td>\n",
              "      <td>8.7800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>405.0</td>\n",
              "      <td>68.241975</td>\n",
              "      <td>27.908527</td>\n",
              "      <td>2.90000</td>\n",
              "      <td>45.00000</td>\n",
              "      <td>76.50000</td>\n",
              "      <td>94.10000</td>\n",
              "      <td>100.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>405.0</td>\n",
              "      <td>3.834098</td>\n",
              "      <td>2.162173</td>\n",
              "      <td>1.12960</td>\n",
              "      <td>2.07880</td>\n",
              "      <td>3.27210</td>\n",
              "      <td>5.21460</td>\n",
              "      <td>12.1265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>405.0</td>\n",
              "      <td>9.474074</td>\n",
              "      <td>8.618564</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>24.00000</td>\n",
              "      <td>24.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>405.0</td>\n",
              "      <td>405.839506</td>\n",
              "      <td>164.057951</td>\n",
              "      <td>187.00000</td>\n",
              "      <td>284.00000</td>\n",
              "      <td>334.00000</td>\n",
              "      <td>666.00000</td>\n",
              "      <td>711.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>405.0</td>\n",
              "      <td>18.437037</td>\n",
              "      <td>2.141275</td>\n",
              "      <td>12.60000</td>\n",
              "      <td>17.30000</td>\n",
              "      <td>18.80000</td>\n",
              "      <td>20.20000</td>\n",
              "      <td>22.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>405.0</td>\n",
              "      <td>356.276444</td>\n",
              "      <td>91.298597</td>\n",
              "      <td>2.60000</td>\n",
              "      <td>375.21000</td>\n",
              "      <td>391.23000</td>\n",
              "      <td>396.24000</td>\n",
              "      <td>396.9000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>405.0</td>\n",
              "      <td>12.638914</td>\n",
              "      <td>7.090281</td>\n",
              "      <td>1.73000</td>\n",
              "      <td>7.19000</td>\n",
              "      <td>11.32000</td>\n",
              "      <td>16.65000</td>\n",
              "      <td>37.9700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    count        mean         std  ...        50%        75%       max\n",
              "0   405.0    3.670339    8.977954  ...    0.26838    3.47428   88.9762\n",
              "1   405.0   11.067901   22.688396  ...    0.00000   12.50000   95.0000\n",
              "2   405.0   11.097951    6.699157  ...    9.69000   18.10000   27.7400\n",
              "3   405.0    0.069136    0.253999  ...    0.00000    0.00000    1.0000\n",
              "4   405.0    0.553098    0.115564  ...    0.53800    0.62400    0.8710\n",
              "5   405.0    6.269768    0.690552  ...    6.19300    6.61900    8.7800\n",
              "6   405.0   68.241975   27.908527  ...   76.50000   94.10000  100.0000\n",
              "7   405.0    3.834098    2.162173  ...    3.27210    5.21460   12.1265\n",
              "8   405.0    9.474074    8.618564  ...    5.00000   24.00000   24.0000\n",
              "9   405.0  405.839506  164.057951  ...  334.00000  666.00000  711.0000\n",
              "10  405.0   18.437037    2.141275  ...   18.80000   20.20000   22.0000\n",
              "11  405.0  356.276444   91.298597  ...  391.23000  396.24000  396.9000\n",
              "12  405.0   12.638914    7.090281  ...   11.32000   16.65000   37.9700\n",
              "\n",
              "[13 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaeT7TKazbzY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f00529e9-a01a-44f5-e51a-8e7abb8b2a0f"
      },
      "source": [
        "train_labels = train_dataset.pop(13)\n",
        "test_labels = test_dataset.pop(13)\n",
        "\n",
        "train_dataset.shape, test_dataset.shape, train_labels.shape, test_labels.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((405, 13), (101, 13), (405,), (101,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXTA-1-azb3-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "cd111bd6-b72d-426d-db87-0a094f1b1753"
      },
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats['mean']) / train_stats['std']\n",
        "\n",
        "normed_train_data = norm(train_dataset)\n",
        "normed_test_data = norm(test_dataset)\n",
        "\n",
        "normed_train_data.shape, normed_test_data.shape, train_labels.shape, test_labels.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((405, 13), (101, 13), (405,), (101,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpWuuTzHzb8T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = normed_train_data.join(train_labels)\n",
        "test = normed_test_data.join(test_labels)\n",
        "X = normed_train_data.values\n",
        "Y = train_labels.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSthi-VISSNi",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXp-AqomffLx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras_model = Sequential([\n",
        "    Dense(64, input_shape=[13], activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(1),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SePBabhfk5D",
        "colab_type": "code",
        "outputId": "5056d61a-2b16-4090-fda7-6cd1fb989d8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "keras_model.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 64)                896       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 3,009\n",
            "Trainable params: 3,009\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfxSyK6xSLsu",
        "colab_type": "text"
      },
      "source": [
        "# Save and Export no Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8SQOLPlfoxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras_model.save('keras_no_train_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKnae_RVfulz",
        "colab_type": "code",
        "outputId": "79851784-1b6f-43ff-b38b-ec8c00902140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "mlmodel = coremltools.converters.keras.convert('keras_no_train_model.h5') # , use_float_arraytype = True)\n",
        "mlmodel.save('coreml_model_double_no_train.mlmodel')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : dense_1_input, <keras.engine.input_layer.InputLayer object at 0x7f41bdb9add8>\n",
            "1 : dense_1, <keras.layers.core.Dense object at 0x7f41bdb9ada0>\n",
            "2 : dense_1__activation__, <keras.layers.core.Activation object at 0x7f41bdb5dba8>\n",
            "3 : dense_2, <keras.layers.core.Dense object at 0x7f41bdb9af98>\n",
            "4 : dense_2__activation__, <keras.layers.core.Activation object at 0x7f41bdb5def0>\n",
            "5 : dense_3, <keras.layers.core.Dense object at 0x7f41bdba6cc0>\n",
            "\n",
            "\n",
            "Recommendation: This model has at least one multiarray input/output of type double.\n",
            "For large sized arrays, multiarrays of type float32 are more efficient.\n",
            "In future, float input/output multiarrays will be produced by default by the converter.\n",
            "Please use, either the flag 'use_float_arraytype' during the call to convert or\n",
            "the utility 'coremltools.utils.convert_double_to_float_multiarray_type(spec)', post-conversion.\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:310: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "  warnings.warn('No training configuration found in save file: '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM_LbFmlSFE3",
        "colab_type": "text"
      },
      "source": [
        "# Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrYe6WYph8p9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = keras.optimizers.RMSprop(0.001)  #Adam()\n",
        "\n",
        "keras_model.compile(loss='mse',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mae', 'mse'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPFbzJFv0LN0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7bf85d4e-8dd0-485a-d181-dfacd7d0e8c5"
      },
      "source": [
        "history = keras_model.fit(X, Y, epochs=500, verbose=1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "405/405 [==============================] - 0s 308us/step - loss: 530.7045 - mean_absolute_error: 21.2605 - mean_squared_error: 530.7045\n",
            "Epoch 2/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 458.5354 - mean_absolute_error: 19.5586 - mean_squared_error: 458.5354\n",
            "Epoch 3/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 385.5517 - mean_absolute_error: 17.6334 - mean_squared_error: 385.5517\n",
            "Epoch 4/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 307.6333 - mean_absolute_error: 15.4750 - mean_squared_error: 307.6333\n",
            "Epoch 5/500\n",
            "405/405 [==============================] - 0s 40us/step - loss: 230.5435 - mean_absolute_error: 13.0359 - mean_squared_error: 230.5435\n",
            "Epoch 6/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 161.3204 - mean_absolute_error: 10.5219 - mean_squared_error: 161.3204\n",
            "Epoch 7/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 106.8344 - mean_absolute_error: 8.3092 - mean_squared_error: 106.8344\n",
            "Epoch 8/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 71.5324 - mean_absolute_error: 6.6265 - mean_squared_error: 71.5324\n",
            "Epoch 9/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 51.4197 - mean_absolute_error: 5.5110 - mean_squared_error: 51.4197\n",
            "Epoch 10/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 39.2856 - mean_absolute_error: 4.7242 - mean_squared_error: 39.2856\n",
            "Epoch 11/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 31.8604 - mean_absolute_error: 4.1903 - mean_squared_error: 31.8604\n",
            "Epoch 12/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 27.6286 - mean_absolute_error: 3.8177 - mean_squared_error: 27.6286\n",
            "Epoch 13/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 24.5279 - mean_absolute_error: 3.5670 - mean_squared_error: 24.5279\n",
            "Epoch 14/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 22.6613 - mean_absolute_error: 3.4083 - mean_squared_error: 22.6613\n",
            "Epoch 15/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 21.0148 - mean_absolute_error: 3.2782 - mean_squared_error: 21.0148\n",
            "Epoch 16/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 19.8662 - mean_absolute_error: 3.1432 - mean_squared_error: 19.8662\n",
            "Epoch 17/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 18.9400 - mean_absolute_error: 3.0631 - mean_squared_error: 18.9400\n",
            "Epoch 18/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 17.9832 - mean_absolute_error: 2.9693 - mean_squared_error: 17.9832\n",
            "Epoch 19/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 17.1975 - mean_absolute_error: 2.8800 - mean_squared_error: 17.1975\n",
            "Epoch 20/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 16.3430 - mean_absolute_error: 2.8302 - mean_squared_error: 16.3430\n",
            "Epoch 21/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 16.1392 - mean_absolute_error: 2.7854 - mean_squared_error: 16.1392\n",
            "Epoch 22/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 15.6157 - mean_absolute_error: 2.7387 - mean_squared_error: 15.6157\n",
            "Epoch 23/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 15.2322 - mean_absolute_error: 2.6898 - mean_squared_error: 15.2322\n",
            "Epoch 24/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 14.8154 - mean_absolute_error: 2.6709 - mean_squared_error: 14.8154\n",
            "Epoch 25/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 14.5101 - mean_absolute_error: 2.6475 - mean_squared_error: 14.5101\n",
            "Epoch 26/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 14.1405 - mean_absolute_error: 2.6322 - mean_squared_error: 14.1405\n",
            "Epoch 27/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 14.0930 - mean_absolute_error: 2.6418 - mean_squared_error: 14.0930\n",
            "Epoch 28/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 13.7816 - mean_absolute_error: 2.5653 - mean_squared_error: 13.7816\n",
            "Epoch 29/500\n",
            "405/405 [==============================] - 0s 66us/step - loss: 13.4667 - mean_absolute_error: 2.5280 - mean_squared_error: 13.4667\n",
            "Epoch 30/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 13.4135 - mean_absolute_error: 2.5123 - mean_squared_error: 13.4135\n",
            "Epoch 31/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 13.1573 - mean_absolute_error: 2.4886 - mean_squared_error: 13.1573\n",
            "Epoch 32/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 13.1123 - mean_absolute_error: 2.5060 - mean_squared_error: 13.1123\n",
            "Epoch 33/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 12.7798 - mean_absolute_error: 2.4811 - mean_squared_error: 12.7798\n",
            "Epoch 34/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 12.6900 - mean_absolute_error: 2.4492 - mean_squared_error: 12.6900\n",
            "Epoch 35/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 12.7824 - mean_absolute_error: 2.4989 - mean_squared_error: 12.7824\n",
            "Epoch 36/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 12.2505 - mean_absolute_error: 2.4036 - mean_squared_error: 12.2505\n",
            "Epoch 37/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 12.2440 - mean_absolute_error: 2.4187 - mean_squared_error: 12.2440\n",
            "Epoch 38/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 12.2339 - mean_absolute_error: 2.4329 - mean_squared_error: 12.2339\n",
            "Epoch 39/500\n",
            "405/405 [==============================] - 0s 39us/step - loss: 12.0336 - mean_absolute_error: 2.3906 - mean_squared_error: 12.0336\n",
            "Epoch 40/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 12.0470 - mean_absolute_error: 2.4006 - mean_squared_error: 12.0470\n",
            "Epoch 41/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 11.6878 - mean_absolute_error: 2.3719 - mean_squared_error: 11.6878\n",
            "Epoch 42/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 11.7744 - mean_absolute_error: 2.3722 - mean_squared_error: 11.7744\n",
            "Epoch 43/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 11.5958 - mean_absolute_error: 2.3368 - mean_squared_error: 11.5958\n",
            "Epoch 44/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 11.4127 - mean_absolute_error: 2.3355 - mean_squared_error: 11.4127\n",
            "Epoch 45/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 11.2925 - mean_absolute_error: 2.3211 - mean_squared_error: 11.2925\n",
            "Epoch 46/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 11.2336 - mean_absolute_error: 2.2950 - mean_squared_error: 11.2336\n",
            "Epoch 47/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 11.2576 - mean_absolute_error: 2.3194 - mean_squared_error: 11.2576\n",
            "Epoch 48/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 10.9409 - mean_absolute_error: 2.2939 - mean_squared_error: 10.9409\n",
            "Epoch 49/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 10.9457 - mean_absolute_error: 2.2747 - mean_squared_error: 10.9457\n",
            "Epoch 50/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 10.8927 - mean_absolute_error: 2.2905 - mean_squared_error: 10.8927\n",
            "Epoch 51/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 10.7784 - mean_absolute_error: 2.2462 - mean_squared_error: 10.7784\n",
            "Epoch 52/500\n",
            "405/405 [==============================] - 0s 38us/step - loss: 10.6105 - mean_absolute_error: 2.2621 - mean_squared_error: 10.6105\n",
            "Epoch 53/500\n",
            "405/405 [==============================] - 0s 39us/step - loss: 10.7495 - mean_absolute_error: 2.2544 - mean_squared_error: 10.7495\n",
            "Epoch 54/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 10.5590 - mean_absolute_error: 2.2607 - mean_squared_error: 10.5590\n",
            "Epoch 55/500\n",
            "405/405 [==============================] - 0s 59us/step - loss: 10.5631 - mean_absolute_error: 2.2162 - mean_squared_error: 10.5631\n",
            "Epoch 56/500\n",
            "405/405 [==============================] - 0s 58us/step - loss: 10.4144 - mean_absolute_error: 2.2191 - mean_squared_error: 10.4144\n",
            "Epoch 57/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 10.3847 - mean_absolute_error: 2.2213 - mean_squared_error: 10.3847\n",
            "Epoch 58/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 10.2596 - mean_absolute_error: 2.2133 - mean_squared_error: 10.2596\n",
            "Epoch 59/500\n",
            "405/405 [==============================] - 0s 62us/step - loss: 10.3046 - mean_absolute_error: 2.2002 - mean_squared_error: 10.3046\n",
            "Epoch 60/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 9.9870 - mean_absolute_error: 2.2111 - mean_squared_error: 9.9870\n",
            "Epoch 61/500\n",
            "405/405 [==============================] - 0s 59us/step - loss: 10.1192 - mean_absolute_error: 2.1925 - mean_squared_error: 10.1192\n",
            "Epoch 62/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 10.0959 - mean_absolute_error: 2.1912 - mean_squared_error: 10.0959\n",
            "Epoch 63/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 9.8634 - mean_absolute_error: 2.1644 - mean_squared_error: 9.8634\n",
            "Epoch 64/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 9.8745 - mean_absolute_error: 2.1853 - mean_squared_error: 9.8745\n",
            "Epoch 65/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 9.8249 - mean_absolute_error: 2.1624 - mean_squared_error: 9.8249\n",
            "Epoch 66/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 9.7934 - mean_absolute_error: 2.1828 - mean_squared_error: 9.7934\n",
            "Epoch 67/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 9.6970 - mean_absolute_error: 2.1650 - mean_squared_error: 9.6970\n",
            "Epoch 68/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 9.6423 - mean_absolute_error: 2.1850 - mean_squared_error: 9.6423\n",
            "Epoch 69/500\n",
            "405/405 [==============================] - 0s 60us/step - loss: 9.5612 - mean_absolute_error: 2.1676 - mean_squared_error: 9.5612\n",
            "Epoch 70/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 9.3813 - mean_absolute_error: 2.1077 - mean_squared_error: 9.3813\n",
            "Epoch 71/500\n",
            "405/405 [==============================] - 0s 60us/step - loss: 9.4726 - mean_absolute_error: 2.1358 - mean_squared_error: 9.4726\n",
            "Epoch 72/500\n",
            "405/405 [==============================] - 0s 65us/step - loss: 9.3321 - mean_absolute_error: 2.1403 - mean_squared_error: 9.3321\n",
            "Epoch 73/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 9.2300 - mean_absolute_error: 2.1079 - mean_squared_error: 9.2300\n",
            "Epoch 74/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 9.2216 - mean_absolute_error: 2.1112 - mean_squared_error: 9.2216\n",
            "Epoch 75/500\n",
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            "Epoch 76/500\n",
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            "Epoch 78/500\n",
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            "Epoch 79/500\n",
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            "Epoch 80/500\n",
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            "Epoch 81/500\n",
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            "Epoch 83/500\n",
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            "Epoch 84/500\n",
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            "Epoch 87/500\n",
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            "Epoch 88/500\n",
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            "Epoch 89/500\n",
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            "Epoch 90/500\n",
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            "Epoch 91/500\n",
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            "Epoch 93/500\n",
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            "Epoch 94/500\n",
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            "Epoch 95/500\n",
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            "Epoch 96/500\n",
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            "Epoch 97/500\n",
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            "Epoch 98/500\n",
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            "Epoch 99/500\n",
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            "Epoch 100/500\n",
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            "Epoch 101/500\n",
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            "Epoch 102/500\n",
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            "Epoch 103/500\n",
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            "Epoch 104/500\n",
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            "Epoch 105/500\n",
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            "Epoch 107/500\n",
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            "Epoch 108/500\n",
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            "Epoch 109/500\n",
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            "Epoch 110/500\n",
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            "Epoch 111/500\n",
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            "Epoch 112/500\n",
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            "Epoch 113/500\n",
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            "Epoch 114/500\n",
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            "Epoch 115/500\n",
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            "Epoch 116/500\n",
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            "Epoch 117/500\n",
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            "Epoch 118/500\n",
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            "Epoch 119/500\n",
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            "Epoch 120/500\n",
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            "Epoch 121/500\n",
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            "Epoch 122/500\n",
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            "Epoch 123/500\n",
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            "Epoch 124/500\n",
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            "Epoch 125/500\n",
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            "Epoch 126/500\n",
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            "Epoch 127/500\n",
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            "Epoch 128/500\n",
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            "Epoch 129/500\n",
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            "Epoch 130/500\n",
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            "Epoch 131/500\n",
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            "Epoch 132/500\n",
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            "Epoch 133/500\n",
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            "Epoch 134/500\n",
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            "Epoch 135/500\n",
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            "Epoch 136/500\n",
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            "Epoch 137/500\n",
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            "Epoch 138/500\n",
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            "Epoch 139/500\n",
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            "Epoch 140/500\n",
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            "Epoch 141/500\n",
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            "Epoch 142/500\n",
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            "Epoch 143/500\n",
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            "Epoch 144/500\n",
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            "Epoch 145/500\n",
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            "Epoch 146/500\n",
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            "Epoch 147/500\n",
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            "Epoch 148/500\n",
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            "Epoch 149/500\n",
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            "Epoch 150/500\n",
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            "Epoch 151/500\n",
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            "Epoch 152/500\n",
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            "Epoch 153/500\n",
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            "Epoch 154/500\n",
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            "Epoch 155/500\n",
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            "Epoch 156/500\n",
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            "Epoch 158/500\n",
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            "Epoch 159/500\n",
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            "Epoch 160/500\n",
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            "Epoch 161/500\n",
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            "Epoch 162/500\n",
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            "Epoch 163/500\n",
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            "Epoch 164/500\n",
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            "Epoch 165/500\n",
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            "Epoch 166/500\n",
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            "Epoch 167/500\n",
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            "Epoch 168/500\n",
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            "Epoch 169/500\n",
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            "Epoch 170/500\n",
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            "Epoch 171/500\n",
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            "Epoch 172/500\n",
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            "Epoch 173/500\n",
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            "Epoch 174/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 5.1347 - mean_absolute_error: 1.6097 - mean_squared_error: 5.1347\n",
            "Epoch 175/500\n",
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            "Epoch 176/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 5.1173 - mean_absolute_error: 1.6283 - mean_squared_error: 5.1173\n",
            "Epoch 177/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 4.9877 - mean_absolute_error: 1.5986 - mean_squared_error: 4.9877\n",
            "Epoch 178/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 5.0808 - mean_absolute_error: 1.6268 - mean_squared_error: 5.0808\n",
            "Epoch 179/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 5.3169 - mean_absolute_error: 1.6494 - mean_squared_error: 5.3169\n",
            "Epoch 180/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 5.0411 - mean_absolute_error: 1.6120 - mean_squared_error: 5.0411\n",
            "Epoch 181/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 5.0668 - mean_absolute_error: 1.5987 - mean_squared_error: 5.0668\n",
            "Epoch 182/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 5.0532 - mean_absolute_error: 1.6241 - mean_squared_error: 5.0532\n",
            "Epoch 183/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 5.0254 - mean_absolute_error: 1.5997 - mean_squared_error: 5.0254\n",
            "Epoch 184/500\n",
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            "Epoch 185/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 4.9420 - mean_absolute_error: 1.5777 - mean_squared_error: 4.9420\n",
            "Epoch 186/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 4.9771 - mean_absolute_error: 1.5957 - mean_squared_error: 4.9771\n",
            "Epoch 187/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 4.9779 - mean_absolute_error: 1.6021 - mean_squared_error: 4.9779\n",
            "Epoch 188/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 4.9480 - mean_absolute_error: 1.6054 - mean_squared_error: 4.9480\n",
            "Epoch 189/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 4.8787 - mean_absolute_error: 1.6023 - mean_squared_error: 4.8787\n",
            "Epoch 190/500\n",
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            "Epoch 191/500\n",
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            "Epoch 192/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 4.9542 - mean_absolute_error: 1.6027 - mean_squared_error: 4.9542\n",
            "Epoch 193/500\n",
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            "Epoch 194/500\n",
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            "Epoch 195/500\n",
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            "Epoch 196/500\n",
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            "Epoch 197/500\n",
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            "Epoch 198/500\n",
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            "Epoch 199/500\n",
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            "Epoch 200/500\n",
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            "Epoch 201/500\n",
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            "Epoch 202/500\n",
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            "Epoch 203/500\n",
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            "Epoch 204/500\n",
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            "Epoch 205/500\n",
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            "Epoch 206/500\n",
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            "Epoch 207/500\n",
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            "Epoch 208/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 4.4583 - mean_absolute_error: 1.5082 - mean_squared_error: 4.4583\n",
            "Epoch 209/500\n",
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            "Epoch 210/500\n",
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            "Epoch 211/500\n",
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            "Epoch 212/500\n",
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            "Epoch 213/500\n",
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            "Epoch 214/500\n",
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            "Epoch 215/500\n",
            "405/405 [==============================] - 0s 68us/step - loss: 4.1971 - mean_absolute_error: 1.4609 - mean_squared_error: 4.1971\n",
            "Epoch 216/500\n",
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            "Epoch 217/500\n",
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            "Epoch 218/500\n",
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            "Epoch 219/500\n",
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            "Epoch 220/500\n",
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            "Epoch 221/500\n",
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            "Epoch 222/500\n",
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            "Epoch 223/500\n",
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            "Epoch 224/500\n",
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            "Epoch 225/500\n",
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            "Epoch 226/500\n",
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            "Epoch 227/500\n",
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            "Epoch 228/500\n",
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            "Epoch 229/500\n",
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            "Epoch 230/500\n",
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            "Epoch 231/500\n",
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            "Epoch 232/500\n",
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            "Epoch 233/500\n",
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            "Epoch 234/500\n",
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            "Epoch 235/500\n",
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            "Epoch 236/500\n",
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            "Epoch 237/500\n",
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            "Epoch 238/500\n",
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            "Epoch 239/500\n",
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            "Epoch 240/500\n",
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            "Epoch 241/500\n",
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            "Epoch 242/500\n",
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            "Epoch 243/500\n",
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            "Epoch 244/500\n",
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            "Epoch 245/500\n",
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            "Epoch 246/500\n",
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            "Epoch 247/500\n",
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            "Epoch 248/500\n",
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            "Epoch 249/500\n",
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            "Epoch 250/500\n",
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            "Epoch 251/500\n",
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            "Epoch 252/500\n",
            "405/405 [==============================] - 0s 67us/step - loss: 3.7100 - mean_absolute_error: 1.3651 - mean_squared_error: 3.7100\n",
            "Epoch 253/500\n",
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            "Epoch 254/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 3.5592 - mean_absolute_error: 1.3454 - mean_squared_error: 3.5592\n",
            "Epoch 255/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 3.8758 - mean_absolute_error: 1.3867 - mean_squared_error: 3.8758\n",
            "Epoch 256/500\n",
            "405/405 [==============================] - 0s 59us/step - loss: 3.5015 - mean_absolute_error: 1.3585 - mean_squared_error: 3.5015\n",
            "Epoch 257/500\n",
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            "Epoch 258/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 3.6379 - mean_absolute_error: 1.3606 - mean_squared_error: 3.6379\n",
            "Epoch 259/500\n",
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            "Epoch 260/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 3.5697 - mean_absolute_error: 1.3474 - mean_squared_error: 3.5697\n",
            "Epoch 261/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 3.6686 - mean_absolute_error: 1.3774 - mean_squared_error: 3.6686\n",
            "Epoch 262/500\n",
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            "Epoch 263/500\n",
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            "Epoch 264/500\n",
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            "Epoch 265/500\n",
            "405/405 [==============================] - 0s 71us/step - loss: 3.4822 - mean_absolute_error: 1.3305 - mean_squared_error: 3.4822\n",
            "Epoch 266/500\n",
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            "Epoch 267/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 3.6305 - mean_absolute_error: 1.3745 - mean_squared_error: 3.6305\n",
            "Epoch 268/500\n",
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            "Epoch 269/500\n",
            "405/405 [==============================] - 0s 66us/step - loss: 3.5519 - mean_absolute_error: 1.3458 - mean_squared_error: 3.5519\n",
            "Epoch 270/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 3.5324 - mean_absolute_error: 1.3329 - mean_squared_error: 3.5324\n",
            "Epoch 271/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 3.3573 - mean_absolute_error: 1.3203 - mean_squared_error: 3.3573\n",
            "Epoch 272/500\n",
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            "Epoch 273/500\n",
            "405/405 [==============================] - 0s 68us/step - loss: 3.4374 - mean_absolute_error: 1.3594 - mean_squared_error: 3.4374\n",
            "Epoch 274/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.3408 - mean_absolute_error: 1.3078 - mean_squared_error: 3.3408\n",
            "Epoch 275/500\n",
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            "Epoch 276/500\n",
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            "Epoch 277/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 3.3267 - mean_absolute_error: 1.3256 - mean_squared_error: 3.3267\n",
            "Epoch 278/500\n",
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            "Epoch 279/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.2724 - mean_absolute_error: 1.2832 - mean_squared_error: 3.2724\n",
            "Epoch 280/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 3.2159 - mean_absolute_error: 1.2754 - mean_squared_error: 3.2159\n",
            "Epoch 281/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 3.2309 - mean_absolute_error: 1.2908 - mean_squared_error: 3.2309\n",
            "Epoch 282/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.4283 - mean_absolute_error: 1.3232 - mean_squared_error: 3.4283\n",
            "Epoch 283/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.2251 - mean_absolute_error: 1.2680 - mean_squared_error: 3.2251\n",
            "Epoch 284/500\n",
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            "Epoch 285/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 3.3255 - mean_absolute_error: 1.3122 - mean_squared_error: 3.3255\n",
            "Epoch 286/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.2762 - mean_absolute_error: 1.2845 - mean_squared_error: 3.2762\n",
            "Epoch 287/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 3.4295 - mean_absolute_error: 1.3185 - mean_squared_error: 3.4295\n",
            "Epoch 288/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 3.1271 - mean_absolute_error: 1.2623 - mean_squared_error: 3.1271\n",
            "Epoch 289/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 3.1498 - mean_absolute_error: 1.3194 - mean_squared_error: 3.1498\n",
            "Epoch 290/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 3.3265 - mean_absolute_error: 1.3185 - mean_squared_error: 3.3265\n",
            "Epoch 291/500\n",
            "405/405 [==============================] - 0s 57us/step - loss: 3.1390 - mean_absolute_error: 1.2753 - mean_squared_error: 3.1390\n",
            "Epoch 292/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 3.3074 - mean_absolute_error: 1.3008 - mean_squared_error: 3.3074\n",
            "Epoch 293/500\n",
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            "Epoch 294/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 3.0200 - mean_absolute_error: 1.2668 - mean_squared_error: 3.0200\n",
            "Epoch 295/500\n",
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            "Epoch 296/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 3.2200 - mean_absolute_error: 1.2917 - mean_squared_error: 3.2200\n",
            "Epoch 297/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 3.1902 - mean_absolute_error: 1.2669 - mean_squared_error: 3.1902\n",
            "Epoch 298/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 3.1584 - mean_absolute_error: 1.2863 - mean_squared_error: 3.1584\n",
            "Epoch 299/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.0558 - mean_absolute_error: 1.2519 - mean_squared_error: 3.0558\n",
            "Epoch 300/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 3.2349 - mean_absolute_error: 1.2790 - mean_squared_error: 3.2349\n",
            "Epoch 301/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 3.1075 - mean_absolute_error: 1.2960 - mean_squared_error: 3.1075\n",
            "Epoch 302/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 3.1332 - mean_absolute_error: 1.2679 - mean_squared_error: 3.1332\n",
            "Epoch 303/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 3.0460 - mean_absolute_error: 1.2389 - mean_squared_error: 3.0460\n",
            "Epoch 304/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 3.1794 - mean_absolute_error: 1.2773 - mean_squared_error: 3.1794\n",
            "Epoch 305/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.9797 - mean_absolute_error: 1.2542 - mean_squared_error: 2.9797\n",
            "Epoch 306/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 3.1119 - mean_absolute_error: 1.2583 - mean_squared_error: 3.1119\n",
            "Epoch 307/500\n",
            "405/405 [==============================] - 0s 60us/step - loss: 3.0054 - mean_absolute_error: 1.2427 - mean_squared_error: 3.0054\n",
            "Epoch 308/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 3.0146 - mean_absolute_error: 1.2285 - mean_squared_error: 3.0146\n",
            "Epoch 309/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 3.1559 - mean_absolute_error: 1.2725 - mean_squared_error: 3.1559\n",
            "Epoch 310/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 3.0040 - mean_absolute_error: 1.2367 - mean_squared_error: 3.0040\n",
            "Epoch 311/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.9488 - mean_absolute_error: 1.2524 - mean_squared_error: 2.9488\n",
            "Epoch 312/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.9164 - mean_absolute_error: 1.2369 - mean_squared_error: 2.9164\n",
            "Epoch 313/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 3.1009 - mean_absolute_error: 1.2405 - mean_squared_error: 3.1009\n",
            "Epoch 314/500\n",
            "405/405 [==============================] - 0s 57us/step - loss: 3.0515 - mean_absolute_error: 1.2426 - mean_squared_error: 3.0515\n",
            "Epoch 315/500\n",
            "405/405 [==============================] - 0s 69us/step - loss: 2.9005 - mean_absolute_error: 1.2109 - mean_squared_error: 2.9005\n",
            "Epoch 316/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.9664 - mean_absolute_error: 1.2261 - mean_squared_error: 2.9664\n",
            "Epoch 317/500\n",
            "405/405 [==============================] - 0s 40us/step - loss: 2.9512 - mean_absolute_error: 1.2366 - mean_squared_error: 2.9512\n",
            "Epoch 318/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.9417 - mean_absolute_error: 1.2354 - mean_squared_error: 2.9417\n",
            "Epoch 319/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.9177 - mean_absolute_error: 1.2355 - mean_squared_error: 2.9177\n",
            "Epoch 320/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.9061 - mean_absolute_error: 1.2231 - mean_squared_error: 2.9061\n",
            "Epoch 321/500\n",
            "405/405 [==============================] - 0s 40us/step - loss: 2.9609 - mean_absolute_error: 1.2448 - mean_squared_error: 2.9609\n",
            "Epoch 322/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.9873 - mean_absolute_error: 1.2521 - mean_squared_error: 2.9873\n",
            "Epoch 323/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.8184 - mean_absolute_error: 1.1868 - mean_squared_error: 2.8184\n",
            "Epoch 324/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.9567 - mean_absolute_error: 1.2441 - mean_squared_error: 2.9567\n",
            "Epoch 325/500\n",
            "405/405 [==============================] - 0s 69us/step - loss: 2.9134 - mean_absolute_error: 1.2019 - mean_squared_error: 2.9134\n",
            "Epoch 326/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 2.8907 - mean_absolute_error: 1.2394 - mean_squared_error: 2.8907\n",
            "Epoch 327/500\n",
            "405/405 [==============================] - 0s 39us/step - loss: 2.8477 - mean_absolute_error: 1.2058 - mean_squared_error: 2.8477\n",
            "Epoch 328/500\n",
            "405/405 [==============================] - 0s 64us/step - loss: 2.7880 - mean_absolute_error: 1.1853 - mean_squared_error: 2.7880\n",
            "Epoch 329/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.8894 - mean_absolute_error: 1.2174 - mean_squared_error: 2.8894\n",
            "Epoch 330/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 2.8425 - mean_absolute_error: 1.2127 - mean_squared_error: 2.8425\n",
            "Epoch 331/500\n",
            "405/405 [==============================] - 0s 56us/step - loss: 2.8884 - mean_absolute_error: 1.2272 - mean_squared_error: 2.8884\n",
            "Epoch 332/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.7627 - mean_absolute_error: 1.1768 - mean_squared_error: 2.7627\n",
            "Epoch 333/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 2.7873 - mean_absolute_error: 1.1943 - mean_squared_error: 2.7873\n",
            "Epoch 334/500\n",
            "405/405 [==============================] - 0s 60us/step - loss: 2.8331 - mean_absolute_error: 1.2274 - mean_squared_error: 2.8331\n",
            "Epoch 335/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.7529 - mean_absolute_error: 1.2052 - mean_squared_error: 2.7529\n",
            "Epoch 336/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 2.7275 - mean_absolute_error: 1.1775 - mean_squared_error: 2.7275\n",
            "Epoch 337/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.7691 - mean_absolute_error: 1.2176 - mean_squared_error: 2.7691\n",
            "Epoch 338/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.7932 - mean_absolute_error: 1.1805 - mean_squared_error: 2.7932\n",
            "Epoch 339/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 2.7348 - mean_absolute_error: 1.1806 - mean_squared_error: 2.7348\n",
            "Epoch 340/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.6598 - mean_absolute_error: 1.1683 - mean_squared_error: 2.6598\n",
            "Epoch 341/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.6078 - mean_absolute_error: 1.1666 - mean_squared_error: 2.6078\n",
            "Epoch 342/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 2.7577 - mean_absolute_error: 1.1982 - mean_squared_error: 2.7577\n",
            "Epoch 343/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.6104 - mean_absolute_error: 1.1536 - mean_squared_error: 2.6104\n",
            "Epoch 344/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.6505 - mean_absolute_error: 1.1596 - mean_squared_error: 2.6505\n",
            "Epoch 345/500\n",
            "405/405 [==============================] - 0s 75us/step - loss: 2.5622 - mean_absolute_error: 1.1406 - mean_squared_error: 2.5622\n",
            "Epoch 346/500\n",
            "405/405 [==============================] - 0s 40us/step - loss: 2.6315 - mean_absolute_error: 1.1789 - mean_squared_error: 2.6315\n",
            "Epoch 347/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.6902 - mean_absolute_error: 1.1890 - mean_squared_error: 2.6902\n",
            "Epoch 348/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.4857 - mean_absolute_error: 1.1334 - mean_squared_error: 2.4857\n",
            "Epoch 349/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.7157 - mean_absolute_error: 1.1877 - mean_squared_error: 2.7157\n",
            "Epoch 350/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.6723 - mean_absolute_error: 1.1833 - mean_squared_error: 2.6723\n",
            "Epoch 351/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.6637 - mean_absolute_error: 1.1729 - mean_squared_error: 2.6637\n",
            "Epoch 352/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.5052 - mean_absolute_error: 1.1357 - mean_squared_error: 2.5052\n",
            "Epoch 353/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.6831 - mean_absolute_error: 1.1730 - mean_squared_error: 2.6831\n",
            "Epoch 354/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 2.5663 - mean_absolute_error: 1.1681 - mean_squared_error: 2.5663\n",
            "Epoch 355/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.6302 - mean_absolute_error: 1.1601 - mean_squared_error: 2.6302\n",
            "Epoch 356/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.4510 - mean_absolute_error: 1.1307 - mean_squared_error: 2.4510\n",
            "Epoch 357/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.5754 - mean_absolute_error: 1.1460 - mean_squared_error: 2.5754\n",
            "Epoch 358/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.6437 - mean_absolute_error: 1.1612 - mean_squared_error: 2.6437\n",
            "Epoch 359/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.5580 - mean_absolute_error: 1.1508 - mean_squared_error: 2.5580\n",
            "Epoch 360/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.4738 - mean_absolute_error: 1.1239 - mean_squared_error: 2.4738\n",
            "Epoch 361/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.5404 - mean_absolute_error: 1.1421 - mean_squared_error: 2.5404\n",
            "Epoch 362/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.5454 - mean_absolute_error: 1.1608 - mean_squared_error: 2.5454\n",
            "Epoch 363/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 2.4946 - mean_absolute_error: 1.1453 - mean_squared_error: 2.4946\n",
            "Epoch 364/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.4452 - mean_absolute_error: 1.1396 - mean_squared_error: 2.4452\n",
            "Epoch 365/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 2.5057 - mean_absolute_error: 1.1406 - mean_squared_error: 2.5057\n",
            "Epoch 366/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 2.4436 - mean_absolute_error: 1.1186 - mean_squared_error: 2.4436\n",
            "Epoch 367/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.4759 - mean_absolute_error: 1.1441 - mean_squared_error: 2.4759\n",
            "Epoch 368/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.4379 - mean_absolute_error: 1.1265 - mean_squared_error: 2.4379\n",
            "Epoch 369/500\n",
            "405/405 [==============================] - 0s 61us/step - loss: 2.4198 - mean_absolute_error: 1.1026 - mean_squared_error: 2.4198\n",
            "Epoch 370/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.5643 - mean_absolute_error: 1.1557 - mean_squared_error: 2.5643\n",
            "Epoch 371/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 2.4652 - mean_absolute_error: 1.1230 - mean_squared_error: 2.4652\n",
            "Epoch 372/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.6628 - mean_absolute_error: 1.1541 - mean_squared_error: 2.6628\n",
            "Epoch 373/500\n",
            "405/405 [==============================] - 0s 57us/step - loss: 2.3812 - mean_absolute_error: 1.0885 - mean_squared_error: 2.3812\n",
            "Epoch 374/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.4667 - mean_absolute_error: 1.1158 - mean_squared_error: 2.4667\n",
            "Epoch 375/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.4693 - mean_absolute_error: 1.1087 - mean_squared_error: 2.4693\n",
            "Epoch 376/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.3969 - mean_absolute_error: 1.1027 - mean_squared_error: 2.3969\n",
            "Epoch 377/500\n",
            "405/405 [==============================] - 0s 39us/step - loss: 2.3990 - mean_absolute_error: 1.1089 - mean_squared_error: 2.3990\n",
            "Epoch 378/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.4520 - mean_absolute_error: 1.1100 - mean_squared_error: 2.4520\n",
            "Epoch 379/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.4161 - mean_absolute_error: 1.1190 - mean_squared_error: 2.4161\n",
            "Epoch 380/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.4455 - mean_absolute_error: 1.1125 - mean_squared_error: 2.4455\n",
            "Epoch 381/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.3877 - mean_absolute_error: 1.1092 - mean_squared_error: 2.3877\n",
            "Epoch 382/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.3282 - mean_absolute_error: 1.0919 - mean_squared_error: 2.3282\n",
            "Epoch 383/500\n",
            "405/405 [==============================] - 0s 61us/step - loss: 2.3584 - mean_absolute_error: 1.1099 - mean_squared_error: 2.3584\n",
            "Epoch 384/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.3364 - mean_absolute_error: 1.1027 - mean_squared_error: 2.3364\n",
            "Epoch 385/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 2.2983 - mean_absolute_error: 1.0768 - mean_squared_error: 2.2983\n",
            "Epoch 386/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.3234 - mean_absolute_error: 1.0977 - mean_squared_error: 2.3234\n",
            "Epoch 387/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.4931 - mean_absolute_error: 1.1401 - mean_squared_error: 2.4931\n",
            "Epoch 388/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.3133 - mean_absolute_error: 1.0799 - mean_squared_error: 2.3133\n",
            "Epoch 389/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.4916 - mean_absolute_error: 1.1282 - mean_squared_error: 2.4916\n",
            "Epoch 390/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.2356 - mean_absolute_error: 1.0688 - mean_squared_error: 2.2356\n",
            "Epoch 391/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.2859 - mean_absolute_error: 1.0739 - mean_squared_error: 2.2859\n",
            "Epoch 392/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.3590 - mean_absolute_error: 1.1140 - mean_squared_error: 2.3590\n",
            "Epoch 393/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.3055 - mean_absolute_error: 1.0969 - mean_squared_error: 2.3055\n",
            "Epoch 394/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 2.2711 - mean_absolute_error: 1.0719 - mean_squared_error: 2.2711\n",
            "Epoch 395/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.2749 - mean_absolute_error: 1.0777 - mean_squared_error: 2.2749\n",
            "Epoch 396/500\n",
            "405/405 [==============================] - 0s 62us/step - loss: 2.4381 - mean_absolute_error: 1.1105 - mean_squared_error: 2.4381\n",
            "Epoch 397/500\n",
            "405/405 [==============================] - 0s 57us/step - loss: 2.1964 - mean_absolute_error: 1.0641 - mean_squared_error: 2.1964\n",
            "Epoch 398/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.3497 - mean_absolute_error: 1.0898 - mean_squared_error: 2.3497\n",
            "Epoch 399/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.2756 - mean_absolute_error: 1.0883 - mean_squared_error: 2.2756\n",
            "Epoch 400/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.2962 - mean_absolute_error: 1.0803 - mean_squared_error: 2.2962\n",
            "Epoch 401/500\n",
            "405/405 [==============================] - 0s 55us/step - loss: 2.1648 - mean_absolute_error: 1.0659 - mean_squared_error: 2.1648\n",
            "Epoch 402/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.2708 - mean_absolute_error: 1.0894 - mean_squared_error: 2.2708\n",
            "Epoch 403/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.1134 - mean_absolute_error: 1.0451 - mean_squared_error: 2.1134\n",
            "Epoch 404/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.3600 - mean_absolute_error: 1.1138 - mean_squared_error: 2.3600\n",
            "Epoch 405/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.2340 - mean_absolute_error: 1.0690 - mean_squared_error: 2.2340\n",
            "Epoch 406/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.1876 - mean_absolute_error: 1.0802 - mean_squared_error: 2.1876\n",
            "Epoch 407/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.1725 - mean_absolute_error: 1.0486 - mean_squared_error: 2.1725\n",
            "Epoch 408/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.2546 - mean_absolute_error: 1.0682 - mean_squared_error: 2.2546\n",
            "Epoch 409/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.1946 - mean_absolute_error: 1.0645 - mean_squared_error: 2.1946\n",
            "Epoch 410/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.1924 - mean_absolute_error: 1.0622 - mean_squared_error: 2.1924\n",
            "Epoch 411/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.3404 - mean_absolute_error: 1.0784 - mean_squared_error: 2.3404\n",
            "Epoch 412/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.1596 - mean_absolute_error: 1.0603 - mean_squared_error: 2.1596\n",
            "Epoch 413/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.2794 - mean_absolute_error: 1.0723 - mean_squared_error: 2.2794\n",
            "Epoch 414/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.1557 - mean_absolute_error: 1.0376 - mean_squared_error: 2.1557\n",
            "Epoch 415/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 2.0961 - mean_absolute_error: 1.0369 - mean_squared_error: 2.0961\n",
            "Epoch 416/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.3333 - mean_absolute_error: 1.0893 - mean_squared_error: 2.3333\n",
            "Epoch 417/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.1400 - mean_absolute_error: 1.0252 - mean_squared_error: 2.1400\n",
            "Epoch 418/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 2.1374 - mean_absolute_error: 1.0566 - mean_squared_error: 2.1374\n",
            "Epoch 419/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 2.1433 - mean_absolute_error: 1.0430 - mean_squared_error: 2.1433\n",
            "Epoch 420/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.1954 - mean_absolute_error: 1.0776 - mean_squared_error: 2.1954\n",
            "Epoch 421/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.1352 - mean_absolute_error: 1.0392 - mean_squared_error: 2.1352\n",
            "Epoch 422/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 2.1384 - mean_absolute_error: 1.0792 - mean_squared_error: 2.1384\n",
            "Epoch 423/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.0745 - mean_absolute_error: 1.0546 - mean_squared_error: 2.0745\n",
            "Epoch 424/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.0951 - mean_absolute_error: 1.0185 - mean_squared_error: 2.0951\n",
            "Epoch 425/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.2169 - mean_absolute_error: 1.0930 - mean_squared_error: 2.2169\n",
            "Epoch 426/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.1239 - mean_absolute_error: 1.0563 - mean_squared_error: 2.1239\n",
            "Epoch 427/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.0762 - mean_absolute_error: 1.0477 - mean_squared_error: 2.0762\n",
            "Epoch 428/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.2217 - mean_absolute_error: 1.0690 - mean_squared_error: 2.2217\n",
            "Epoch 429/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.1611 - mean_absolute_error: 1.0591 - mean_squared_error: 2.1611\n",
            "Epoch 430/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 2.0361 - mean_absolute_error: 1.0279 - mean_squared_error: 2.0361\n",
            "Epoch 431/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.0503 - mean_absolute_error: 1.0349 - mean_squared_error: 2.0503\n",
            "Epoch 432/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.1550 - mean_absolute_error: 1.0648 - mean_squared_error: 2.1550\n",
            "Epoch 433/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 1.9991 - mean_absolute_error: 1.0056 - mean_squared_error: 1.9991\n",
            "Epoch 434/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 2.1984 - mean_absolute_error: 1.0759 - mean_squared_error: 2.1984\n",
            "Epoch 435/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.2141 - mean_absolute_error: 1.0664 - mean_squared_error: 2.2141\n",
            "Epoch 436/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 2.0465 - mean_absolute_error: 1.0217 - mean_squared_error: 2.0465\n",
            "Epoch 437/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.0918 - mean_absolute_error: 1.0226 - mean_squared_error: 2.0918\n",
            "Epoch 438/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 2.0318 - mean_absolute_error: 1.0197 - mean_squared_error: 2.0318\n",
            "Epoch 439/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 1.9927 - mean_absolute_error: 1.0149 - mean_squared_error: 1.9927\n",
            "Epoch 440/500\n",
            "405/405 [==============================] - 0s 40us/step - loss: 2.1230 - mean_absolute_error: 1.0398 - mean_squared_error: 2.1230\n",
            "Epoch 441/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.0994 - mean_absolute_error: 1.0503 - mean_squared_error: 2.0994\n",
            "Epoch 442/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.0310 - mean_absolute_error: 1.0080 - mean_squared_error: 2.0310\n",
            "Epoch 443/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.0310 - mean_absolute_error: 1.0383 - mean_squared_error: 2.0310\n",
            "Epoch 444/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 1.9152 - mean_absolute_error: 0.9974 - mean_squared_error: 1.9152\n",
            "Epoch 445/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.0240 - mean_absolute_error: 1.0436 - mean_squared_error: 2.0240\n",
            "Epoch 446/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.1114 - mean_absolute_error: 1.0299 - mean_squared_error: 2.1114\n",
            "Epoch 447/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.0678 - mean_absolute_error: 1.0450 - mean_squared_error: 2.0678\n",
            "Epoch 448/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.9497 - mean_absolute_error: 0.9812 - mean_squared_error: 1.9497\n",
            "Epoch 449/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 1.9225 - mean_absolute_error: 0.9905 - mean_squared_error: 1.9225\n",
            "Epoch 450/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.0479 - mean_absolute_error: 1.0255 - mean_squared_error: 2.0479\n",
            "Epoch 451/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.8560 - mean_absolute_error: 0.9803 - mean_squared_error: 1.8560\n",
            "Epoch 452/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 2.1072 - mean_absolute_error: 1.0525 - mean_squared_error: 2.1072\n",
            "Epoch 453/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 1.9798 - mean_absolute_error: 1.0121 - mean_squared_error: 1.9798\n",
            "Epoch 454/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 1.9550 - mean_absolute_error: 1.0044 - mean_squared_error: 1.9550\n",
            "Epoch 455/500\n",
            "405/405 [==============================] - 0s 53us/step - loss: 2.0276 - mean_absolute_error: 1.0101 - mean_squared_error: 2.0276\n",
            "Epoch 456/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.8441 - mean_absolute_error: 0.9740 - mean_squared_error: 1.8441\n",
            "Epoch 457/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.0317 - mean_absolute_error: 1.0226 - mean_squared_error: 2.0317\n",
            "Epoch 458/500\n",
            "405/405 [==============================] - 0s 52us/step - loss: 1.9613 - mean_absolute_error: 0.9998 - mean_squared_error: 1.9613\n",
            "Epoch 459/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.8499 - mean_absolute_error: 0.9633 - mean_squared_error: 1.8499\n",
            "Epoch 460/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.9179 - mean_absolute_error: 0.9862 - mean_squared_error: 1.9179\n",
            "Epoch 461/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 2.0145 - mean_absolute_error: 1.0099 - mean_squared_error: 2.0145\n",
            "Epoch 462/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 1.8694 - mean_absolute_error: 1.0038 - mean_squared_error: 1.8694\n",
            "Epoch 463/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 2.0546 - mean_absolute_error: 1.0426 - mean_squared_error: 2.0546\n",
            "Epoch 464/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 2.0211 - mean_absolute_error: 1.0245 - mean_squared_error: 2.0211\n",
            "Epoch 465/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 1.8990 - mean_absolute_error: 0.9995 - mean_squared_error: 1.8990\n",
            "Epoch 466/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 2.1113 - mean_absolute_error: 1.0507 - mean_squared_error: 2.1113\n",
            "Epoch 467/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.9780 - mean_absolute_error: 1.0315 - mean_squared_error: 1.9780\n",
            "Epoch 468/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 1.8094 - mean_absolute_error: 0.9704 - mean_squared_error: 1.8094\n",
            "Epoch 469/500\n",
            "405/405 [==============================] - 0s 51us/step - loss: 1.9871 - mean_absolute_error: 1.0032 - mean_squared_error: 1.9871\n",
            "Epoch 470/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.8418 - mean_absolute_error: 0.9744 - mean_squared_error: 1.8418\n",
            "Epoch 471/500\n",
            "405/405 [==============================] - 0s 49us/step - loss: 1.8371 - mean_absolute_error: 0.9590 - mean_squared_error: 1.8371\n",
            "Epoch 472/500\n",
            "405/405 [==============================] - 0s 59us/step - loss: 1.9707 - mean_absolute_error: 1.0179 - mean_squared_error: 1.9707\n",
            "Epoch 473/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 1.9198 - mean_absolute_error: 0.9900 - mean_squared_error: 1.9198\n",
            "Epoch 474/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.8124 - mean_absolute_error: 0.9550 - mean_squared_error: 1.8124\n",
            "Epoch 475/500\n",
            "405/405 [==============================] - 0s 50us/step - loss: 1.9450 - mean_absolute_error: 1.0025 - mean_squared_error: 1.9450\n",
            "Epoch 476/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 1.9231 - mean_absolute_error: 0.9933 - mean_squared_error: 1.9231\n",
            "Epoch 477/500\n",
            "405/405 [==============================] - 0s 54us/step - loss: 1.8033 - mean_absolute_error: 0.9478 - mean_squared_error: 1.8033\n",
            "Epoch 478/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.8714 - mean_absolute_error: 0.9844 - mean_squared_error: 1.8714\n",
            "Epoch 479/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 1.9247 - mean_absolute_error: 0.9781 - mean_squared_error: 1.9247\n",
            "Epoch 480/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.9579 - mean_absolute_error: 0.9962 - mean_squared_error: 1.9579\n",
            "Epoch 481/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 1.7182 - mean_absolute_error: 0.9535 - mean_squared_error: 1.7182\n",
            "Epoch 482/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 1.9149 - mean_absolute_error: 0.9857 - mean_squared_error: 1.9149\n",
            "Epoch 483/500\n",
            "405/405 [==============================] - 0s 48us/step - loss: 1.8801 - mean_absolute_error: 0.9659 - mean_squared_error: 1.8801\n",
            "Epoch 484/500\n",
            "405/405 [==============================] - 0s 44us/step - loss: 1.7851 - mean_absolute_error: 0.9375 - mean_squared_error: 1.7851\n",
            "Epoch 485/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.8744 - mean_absolute_error: 0.9555 - mean_squared_error: 1.8744\n",
            "Epoch 486/500\n",
            "405/405 [==============================] - 0s 67us/step - loss: 1.9096 - mean_absolute_error: 0.9972 - mean_squared_error: 1.9096\n",
            "Epoch 487/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 1.7513 - mean_absolute_error: 0.9337 - mean_squared_error: 1.7513\n",
            "Epoch 488/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.7403 - mean_absolute_error: 0.9274 - mean_squared_error: 1.7403\n",
            "Epoch 489/500\n",
            "405/405 [==============================] - 0s 45us/step - loss: 2.0080 - mean_absolute_error: 1.0534 - mean_squared_error: 2.0080\n",
            "Epoch 490/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.7400 - mean_absolute_error: 0.9390 - mean_squared_error: 1.7400\n",
            "Epoch 491/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.8156 - mean_absolute_error: 0.9499 - mean_squared_error: 1.8156\n",
            "Epoch 492/500\n",
            "405/405 [==============================] - 0s 43us/step - loss: 1.7413 - mean_absolute_error: 0.9503 - mean_squared_error: 1.7413\n",
            "Epoch 493/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.7223 - mean_absolute_error: 0.9283 - mean_squared_error: 1.7223\n",
            "Epoch 494/500\n",
            "405/405 [==============================] - 0s 41us/step - loss: 1.8713 - mean_absolute_error: 0.9782 - mean_squared_error: 1.8713\n",
            "Epoch 495/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 1.8340 - mean_absolute_error: 0.9914 - mean_squared_error: 1.8340\n",
            "Epoch 496/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 1.7740 - mean_absolute_error: 0.9855 - mean_squared_error: 1.7740\n",
            "Epoch 497/500\n",
            "405/405 [==============================] - 0s 42us/step - loss: 1.8944 - mean_absolute_error: 0.9973 - mean_squared_error: 1.8944\n",
            "Epoch 498/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.7055 - mean_absolute_error: 0.9277 - mean_squared_error: 1.7055\n",
            "Epoch 499/500\n",
            "405/405 [==============================] - 0s 46us/step - loss: 1.9063 - mean_absolute_error: 1.0035 - mean_squared_error: 1.9063\n",
            "Epoch 500/500\n",
            "405/405 [==============================] - 0s 47us/step - loss: 1.7536 - mean_absolute_error: 0.9565 - mean_squared_error: 1.7536\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcSVeJbuR9kf",
        "colab_type": "text"
      },
      "source": [
        "# Eval Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVMDnswT0LTq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "47145260-b66b-41c4-abdc-d97cbee5bd22"
      },
      "source": [
        "loss, mae, mse = keras_model.evaluate(normed_test_data.values, test_labels.values, verbose=2)\n",
        "\n",
        "print(\"Loss : {:5.2f}\".format(loss))\n",
        "print(\"Mae : {:5.2f}\".format(mae))\n",
        "print(\"Mse : {:5.2f}\".format(mse))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loss :  6.28\n",
            "Mae :  1.86\n",
            "Mse :  6.28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGKFVaKg0LQ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "0033acb1-f152-449d-a454-d2bcf5882167"
      },
      "source": [
        "score = keras_model.predict(X)\n",
        "print(score.shape)\n",
        "print(score[0])\n",
        "print(X[0], Y[0])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(405, 1)\n",
            "[22.457115]\n",
            "[-0.40132746 -0.48782211 -1.17297605 -0.27218959 -0.80559453  0.09156749\n",
            " -1.82890254  0.63847899 -0.63514919  0.14726805 -0.71781379  0.20738057\n",
            " -0.74734892] 22.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ND9U-IqIR0C0",
        "colab_type": "text"
      },
      "source": [
        "# Test Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFMmRT6D0-7p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "b829b262-1d6f-4ab4-a8c1-472b45cd7713"
      },
      "source": [
        "print(normed_test_data.values[0], test_labels.values[0], keras_model.predict(np.array([normed_test_data.values[0]])))\n",
        "print(normed_test_data.values[1], test_labels.values[1], keras_model.predict(np.array([normed_test_data.values[1]])))\n",
        "print(normed_test_data.values[17], test_labels.values[17], keras_model.predict(np.array([normed_test_data.values[17]])))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.38987719  0.06312032 -0.48184431 -0.27218959 -0.25178915 -0.38486309\n",
            "  0.63271075  1.27556958 -0.51912059 -0.5780854  -1.51173314  0.33334089\n",
            "  0.62918333] 18.9 [[19.53779]]\n",
            "[-0.31519425 -0.48782211 -0.44154073 -0.27218959 -0.13064423 -0.97135058\n",
            "  0.62554448  0.28698074 -0.63514919 -0.60246703  1.19693287 -0.57894038\n",
            "  0.54597083] 13.9 [[15.569779]]\n",
            "[-0.4044373  -0.48782211 -1.14759972 -0.27218959 -0.55465147  0.19583194\n",
            "  0.20273463 -0.34317238 -0.86720641 -0.82799709 -0.29750355  0.40825989\n",
            " -0.62605612] 22.0 [[24.838232]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFk7BGJUShsD",
        "colab_type": "text"
      },
      "source": [
        "# Save and Export Trained Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFCBWdyO0_AX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "f6458082-19e9-452a-d794-ca4890e29061"
      },
      "source": [
        "keras_model.save('keras_train_model.h5')\n",
        "mlmodel = coremltools.converters.keras.convert('keras_train_model.h5') # , use_float_arraytype = True)\n",
        "mlmodel.save('coreml_model_double_train.mlmodel')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : dense_1_input, <keras.engine.input_layer.InputLayer object at 0x7f41bbf792e8>\n",
            "1 : dense_1, <keras.layers.core.Dense object at 0x7f41bbf79c50>\n",
            "2 : dense_1__activation__, <keras.layers.core.Activation object at 0x7f41bc81a748>\n",
            "3 : dense_2, <keras.layers.core.Dense object at 0x7f41bbf79438>\n",
            "4 : dense_2__activation__, <keras.layers.core.Activation object at 0x7f41bbe6ef98>\n",
            "5 : dense_3, <keras.layers.core.Dense object at 0x7f41bbeeba58>\n",
            "\n",
            "\n",
            "Recommendation: This model has at least one multiarray input/output of type double.\n",
            "For large sized arrays, multiarrays of type float32 are more efficient.\n",
            "In future, float input/output multiarrays will be produced by default by the converter.\n",
            "Please use, either the flag 'use_float_arraytype' during the call to convert or\n",
            "the utility 'coremltools.utils.convert_double_to_float_multiarray_type(spec)', post-conversion.\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsek-y7o0_FM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1dba7556-c31d-4323-ba82-388b5f246c3c"
      },
      "source": [
        "spec = mlmodel.get_spec()\n",
        "spec"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "specificationVersion: 1\n",
              "description {\n",
              "  input {\n",
              "    name: \"input1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 13\n",
              "        dataType: DOUBLE\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  output {\n",
              "    name: \"output1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: DOUBLE\n",
              "      }\n",
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              "  metadata {\n",
              "    userDefined {\n",
              "      key: \"coremltoolsVersion\"\n",
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              "  layers {\n",
              "    name: \"dense_1\"\n",
              "    input: \"input1\"\n",
              "    output: \"dense_1_output\"\n",
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              "      inputChannels: 13\n",
              "      outputChannels: 64\n",
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              "      }\n",
              "      bias {\n",
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              "        floatValue: -0.0333738848567009\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  layers {\n",
              "    name: \"dense_2__activation__\"\n",
              "    input: \"dense_2_output\"\n",
              "    output: \"dense_2__activation___output\"\n",
              "    activation {\n",
              "      ReLU {\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  layers {\n",
              "    name: \"dense_3\"\n",
              "    input: \"dense_2__activation___output\"\n",
              "    output: \"output1\"\n",
              "    innerProduct {\n",
              "      inputChannels: 32\n",
              "      outputChannels: 1\n",
              "      hasBias: true\n",
              "      weights {\n",
              "        floatValue: 0.6080498695373535\n",
              "        floatValue: -1.1741865873336792\n",
              "        floatValue: -1.1614378690719604\n",
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              "        floatValue: -1.5157175064086914\n",
              "        floatValue: -0.12000177055597305\n",
              "      }\n",
              "      bias {\n",
              "        floatValue: 0.20288509130477905\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lpnb4pg20_KL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Z5yfeAk0LWx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}